1. Field
The present inventive concept relates generally to a method and apparatus for displaying images, and more particularly, to a method and apparatus for reconstructing images.
2. Description of the Related Art
Images may suffer from a loss of frequency components in their compression and scaling process. Due to the loss of frequency components, images with fine textures may deteriorate into images with coarse and plain textures, resulting in poor visual impressions. Many methods have been developed to compensate for this degradation. Among them, image reconstruction-based methods reconstruct the deteriorated frequency components by way of a modeling a process in which a best solution is found for images which are deteriorated. Sharpening-based methods, which are heuristic methods, amplify a magnitude of the frequency components. Although both the image reconstruction-based methods and the sharpening-based methods improve the overall image sharpness by virtue of the amplification of frequency components, there are limitations on reconstructing the high-frequency components that have already been lost, having difficulty in reconstructing even the fine textures of the original images.
FIG. 1 illustrates an image reconstruction-based method proposed by Freeman.
In FIG. 1, YLR denotes a low-resolution image, YILR denotes a magnified (or up-scaled) low-resolution image, YILR,HF denotes a high frequency component of the magnified low-resolution image, and YDB,HF denotes a high frequency component stored in a database (DB).
Referring to FIG. 1, information relating to a previously learned pair of image patches of an intermediate frequency and a high frequency are stored in an external DB, and an intermediate frequency component of an initially magnified image is replaced by a high frequency component through the use of the DB. That is, a high frequency component YDB,HF is read from the external DB, and a blurred frequency component YILR,HF of a magnified image is replaced by the read high frequency component YDB,HF. In this method, the number of pieces of data to be previously learned in the DB is more than 0.2 million, and a memory for storing the learned data is required. In addition, a computation for comparing YILR,HF of the magnified image with all high frequency components YDB,HF in the DB to find an optimal patch in every reconstruction is required even in a process of reconstructing an image, which are considerably complex. Even in terms of reconstruction performance, the image reconstruction-based method has limitations on reconstructing statistical components having a low correlation with surroundings, such as a texture, because it uses a Markov random field that analogizes and estimates a brightness value of a current location based on previously reconstructed surrounding brightness values. The sharpening-based method also has limitations on reconstructing high frequency components, since it is a method of amplifying frequency components.